Abstract
Rough set theory due to Zdzisław Pawlak (1926-2006) [106, 108, 109, 110], is a mathematical approach to imperfect knowledge. The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it has become a crucial issue for computer scientists as well, particularly in the area of computational intelligence [129], [99]. There are many approaches to the problem of how to understand and manipulate imperfect knowledge. The most successful one is, no doubt, the fuzzy set theory proposed by Lotfi A. Zadeh [226]. Rough set theory presents still another attempt to solve this problem. It is based on an assumption that objects are perceived by partial information about them. Due to this some objects can be indiscernible. Indiscernible objects form elementary granules. From this fact it follows that some sets can not be exactly described by available information about objects. They are rough not crisp. Any rough set is characterized by its (lower and upper) approximations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Stepaniuk, J. (2009). Rough Sets. In: Rough – Granular Computing in Knowledge Discovery and Data Mining. Studies in Computational Intelligence, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70801-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-70801-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70800-1
Online ISBN: 978-3-540-70801-8
eBook Packages: EngineeringEngineering (R0)